Los Alamos National LaboratoryInformation Science and Technology Institute (ISTI)
Implementing and fostering collaborative research, workforce and program development, and technical exchange

Applied Machine Learning Summer Research Fellowship

Creating Next-Generation Leaders in Machine Learning
September 13, 2017
AML Group

Contacts  

  • Program Lead
  • Nick Lubbers
  • Program Co-Lead
  • Youzuo Lin
  • Program Co-Lead
  • Lissa Moore
  • Program Co-Lead
  • Diane Oyen
  • Professional Staff Assistant
  • Natalie Glass

School Inquiries  

We are in the process of planning the AML Summer Research program for 2021. The AML Summer Research program was completed entirely remotely in 2020 due to COVID-19, and we are prepared to function remotely again in 2021 if needed. Our website will be updated with new projects for summer 2021 soon, and applications will be opening in December. Please check back for more information. In the meantime, feel free to browse this past summer’s projects and application process.

 

 The Applied Machine Learning Summer Research Fellowship is an intense 10-12 week program aimed at providing graduate students with a solid foundation in modern machine learning through applications of importance to the National Lab. Projects include developing methodologies to address practical use of machine learning including scalability, transparency, robustness and extendibility. Projects will apply machine learning to problems in tensor decomposition for unsupervised learning, analysis of scientific images, applications to geosciences such as inverse modeling problems, and computational physics. This is a paid fellowship that includes reimbursement for travel expenses.

The program is sponsored by the Information Science and Technology Institute (ISTI) and the Center for Space and Earth Sciences (CSES).

Description

Research Fellows will learn hands-on by engaging in scientific research using machine learning. Research will be performed in small collaborations, guided by mentors with scientific and computational expertise.

See list of projects with descriptions.

Students will work on high performance computing clusters, apply practical ML tools, and gain experience in communicating their work through discussions and presentations. Students will attend seminars by LANL researchers and external visitors. We aim for high-impact summer projects that will lead to peer-reviewed, co-authored publications.

Students

This multi-disciplinary program is designed for graduate students from all science, math, computer science, and technology fields who are seeking to incorporate machine learning into their research careers. As a general guideline, students should have a background in one of the following: probability theory, statistical methods, algorithms, or statistical learning. Experience with programming and machine learning packages is encouraged. Specific skills needed for each project are listed in the project descriptions and the application form asks which projects you are most interested in.

To apply, submit:

  • Letter of intent stating strengths, goals, interests, and how the AML fellowship will help you achieve your goals
  • Current resume / CV
  • Unofficial university transcripts (official transcripts will be required if position offered and accepted)
  • Letter of recommendation from a faculty member

Application Deadline- January 3, 2020. [No longer accepting applications for 2020]

Duration & Location

The 2020 program starts June 1st, 2020. Students can choose to attend for 10-12 weeks. (Some flexibility on these dates is possible for extenuating circumstances.)

Eligibility Requirements

  • Must be accepted to or enrolled in a graduate degree program
  • Must have and maintain a cumulative G.P.A. of 3.2/4.0 or better 
  • Must be available to live and work in Los Alamos, New Mexico

Questions?

More information about living and working in Los Alamos.

See our FAQs page, or contact us.